Intermittent process fault monitoring method based on fourth-order moment singular value decomposition

A singular value decomposition and fault monitoring technology, applied in the field of fourth-order moment singular value decomposition technology, can solve problems such as the impact of fault diagnosis and the destruction of structural information between data

Active Publication Date: 2019-10-01
BEIJING UNIV OF TECH
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The kernel can map data to high dimensions, but at the same time, the structural informa

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  • Intermittent process fault monitoring method based on fourth-order moment singular value decomposition
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  • Intermittent process fault monitoring method based on fourth-order moment singular value decomposition

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Embodiment Construction

[0062] The algorithm proposed in this paper can monitor the faults that occur in the production of industrial batch processes. By processing the fourth moment of variables, performing singular value decomposition, constructing statistics and corresponding control lines to complete the monitoring. Finally, satisfactory monitoring results can be obtained to ensure the safety of production.

[0063] In order to verify the accuracy of the algorithm proposed in this paper, a test was carried out using TE process data. TE (TenesseeEastman) simulation platform is a simulation platform based on the actual chemical reaction process. The data generated by it has time-varying, strong coupling and nonlinear characteristics, and is widely used to test the control and fault diagnosis models of complex industrial processes.

[0064] There are 21 kinds of faults in the TE process, the specific description is shown in Table 1

[0065] Table 1 TE process failure list

[0066]

[0067] The...

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Abstract

The invention discloses an intermittent process fault monitoring method based on fourth-order moment singular value decomposition, and is used for solving data nonlinearity and non-Gaussianity broughtby nonlinearity in an intermittent process. The method comprises two stages of "offline modeling" and "online monitoring", wherein the stage of "offline modeling" comprises the following steps that:firstly, carrying out data standardization, carrying out fourth-order moment processing, and combining fourth-order moment matrixes; and then, carrying out singular value decomposition, and simplifying the obtained matrix to make a preparation for monitoring. The stage of "online monitoring" comprises the following steps that: carrying out standardization on online data, carrying out fourth-ordermoment processing, and combining the fourth-order moment matrixes; and then, calculating a statistical amount, a residual error and a corresponding control line; and finally, using the statistical amount to monitor a generation process, and giving an alarm when faults appear. The method fully considers the nonlinearity and the non-Gaussianity of the data of the intermittent process, reduces the false alarm rate of a normal stage, reduces the false alarm rate of a fault stage, quickens response speed and has a high practical value.

Description

technical field [0001] The invention belongs to the field of industrial process fault monitoring, and in particular relates to a fourth-order moment singular value decomposition technology. The method for fault monitoring based on the fourth-order moment singular value decomposition of the present invention is a specific application in the TE (Tenessee Eastman) process. Background technique [0002] There are a large number of batch processes in modern industrial processes. Common batch processes include microbial pharmaceuticals, sewage treatment, beer preparation, yogurt preparation, etc. The production batch size of the batch process is relatively flexible, and the process change is relatively easy. At the same time, it has certain compatibility for product switching, and can produce a small amount of different varieties, and can quickly adapt to changes in raw materials or operating conditions. [0003] Most industrial process data has strong nonlinearity. Due to the no...

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Application Information

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IPC IPC(8): G05B19/418G06F17/50
CPCG05B19/41885G05B19/41875G06F30/20Y02P90/02
Inventor 常鹏卢瑞炜张祥宇王普
Owner BEIJING UNIV OF TECH
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